Dr. Amy Shiroma Profile

Dr. Amy Shiroma

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Publications (2)

Proceedings Article | 18 October 2022 Open Access

Design and optimization of 3D VSHARP® scatter correction for industrial CBCT using the Linear Boltzmann Transport Equation

Kevin Holt, Devang Savaliya, Amy Shiroma, Martin Hu, David Nisius, Steve Hoelzer, Mingshan Sun, Don Vernekohl, Josh Star-Lack

Proceedings Volume 12304, 1230419 (2022) https://doi.org/10.1117/12.2647154

KEYWORDS: Sensors, Image segmentation, Image quality, Reconstruction algorithms, 3D image processing, Spatial resolution, Genetic algorithms, Aluminum, 3D acquisition, Physics

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We have developed VSHARP®, a suite of scatter correction solutions that have been incorporated into the commercially available cone-beam software development toolkit, CST (Varex Imaging, Salt Lake City, UT) enabling scatter correction to be applied as part of an entire CBCT reconstruction pipeline. The suite includes 2D VSHARP®, a deconvolution correction using asymmetric Gaussian kernels, 2D VSHARP-ML, a U-NET machine-learning correction, and 3D VSHARP®, a correction using a rapid finite-element Linear Boltzmann Transport Equation (LBTE) solver to estimate scatter in a manner similar to traditional stochastic Monte Carlo (MC) simulations. Of the three corrections, 3D VSHARP is the most accurate and flexible since it can be readily applied to arbitrary scanner geometries, protocols, and scan parts while the 2D VSHARP models may need to be regenerated for each configuration. On the other hand, 3D VSHARP is inherently slower since a minimum of two reconstruction passes are needed and the LBTE solver, while much faster than traditional MC, is still computationally intensive. The goal of this work was to minimize LBTE run times for (typically large) industrial datasets by optimizing parameter settings, particularly the choice of the sampling grid dimensions. This was achieved by applying a multi-objective genetic algorithm to find the Pareto front characterizing the tradeoff between speed and accuracy and identifying key operating points on the curve. Testing with 720 frames of 3720x3720 projection data to make a reconstruction volume of size 500x500x600, we found that excellent image quality can be obtained by using a coarse scatter grid size of 27x27x32 volume and 44x44 detector and a primary grid size of 246x246 x295 volume and 295x295 detector, both over 42 frames for a grand total of 21 seconds LBTE computation time. We show the Pareto characterization, as well as demonstrations of 3D VSHARP image quality with significantly reduced scatter-induced artifacts such as streaking and shading.

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Proceedings Article | 4 March 2019 Presentation + Paper

Dual energy imaging with a dual-layer flat panel detector

Minghui Lu, Adam Wang, Edward Shapiro, Amy Shiroma, Jin Zhang, Joachim Steiger, Josh Star-Lack

Proceedings Volume 10948, 1094815 (2019) https://doi.org/10.1117/12.2513499

KEYWORDS: Sensors, Dual energy imaging, Modulation transfer functions, Bone, X-rays, Scintillators, Calibration, Imaging systems, X-ray detectors, X-ray imaging

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Dual Energy (DE) imaging has been widely used in digital radiography and fluoroscopy, as has dual energy CT for various medical applications. In this study, the imaging performance of a dynamic dual-layer a-Si flat panel detector (FPD) prototype was characterized for dual energy imaging tasks. Dual energy cone beam CT (DE CBCT) scans were acquired and used to perform material decomposition in the projection domain, followed by reconstruction to generate material specific and virtual monoenergetic (VM) images. The dual-layer FPD prototype was built on a Varex XRD 4343RF detector by adding a 200 μm thick CsI scintillator and a-Si panel of 150 μm pixel size on top as a low energy detector. A 1 mm copper filter was added as a middle layer to increase energy separation with the bottom layer as a high energy detector. The imaging performance, such as Modulation Transfer Function (MTF), Conversion Factor (CF), and Detector Quantum Efficiency (DQE) of both the top and bottom detector layers were characterized and compared with those of the standard single layer XRD4343 RF detector. Several tissue equivalent cylinders (solid water, liquid water, bone, acrylic, polyethylene, etc.) were placed on a rotating stand, and two separate 450-projection CBCT scans were performed under continuous 120 kV and 80 kV X-ray beams. After an empirical material decomposition calibration, water and bone images were generated for each projection, and their respective volumes were reconstructed using Varex’s CBCT Software Tools (CST 2.0). A VM image, which maximized the contrast-to-noise ratio of water to polyethylene, was generated based on the water and bone images. The MTF at 1.0 lp/mm from the low energy detector was 32% and 22% higher than the high energy detector and the standard detector, respectively; the DQE of both high and low energy detectors is much lower than that of the standard XRD 4343RF detector. The CNR of water to polyethylene from the VM image improved by 50% over that from the low energy image alone at 120 kV, and by 80% at 80 kV. This study demonstrates the feasibility of using a dual-layer FPD in applications such as DE CBCT for contrast enhancement and material decomposition. Further evaluations are underway.

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